Making good choices with variable information: a stochastic model for nest-site selection by honeybees

Area of Science:

• Behavioral Ecology
• Computational Biology
• Animal Communication

Background:

• Honeybee (Apis mellifera) swarms rely on scout bees to locate new nest sites.
• Information transfer and decision-making processes among scouts are crucial for successful colony relocation.
• Understanding the factors influencing nest-site selection can reveal insights into collective intelligence in social insects.

Purpose of the Study:

• To develop a computational model simulating information transfer during honeybee nest-site selection.
• To investigate the influence of site quality, inter-site competition, and discovery delays on the selection process.
• To evaluate the role of stochasticity in honeybee collective decision-making for nest site choice.

Main Methods:

• Construction of a density-dependent Markov process model.
• Simulation of information exchange among scout bees.
• Analysis of model outputs under varying conditions of site quality and discovery time.

Main Results:

• The model predicts more reliable selection of the better site when both options are of low quality compared to high quality.
• Delays in discovering a second potential nest site have the greatest impact on the final choice when both sites are of high quality.
• Stochastic elements within the model do not confer an adaptive advantage to the honeybee swarm.

Conclusions:

• The dynamics of information transfer and site assessment significantly influence honeybee nest-site selection outcomes.
• Site quality and temporal factors (delays) play critical roles in the decision-making process, with complex interactions.
• Randomness in the decision-making process does not appear to enhance swarm success in nest-site selection, suggesting deterministic factors are more critical.